def test_gaussian_sklearn(self):
n = 100
p = 20
k = 3
family = "gaussian"
rho = 0.5
sigma = 1
M = 1
np.random.seed(2)
# data = gen_data(family=family, n=n, p=p, k=k, rho=rho, M=M)
data = gen_data(n, p, family=family, k=k, rho=rho)
# data3 = gen_data_splicing(
# family=family, n=n, p=p, k=k, rho=rho, M=M, sparse_ratio=0.1)
s_max = 20
support_size = np.linspace(0, s_max, s_max + 1)
alpha = [0., 0.1, 0.2, 0.3, 0.4]
model = abessLm()
cv = KFold(n_splits=5, shuffle=True, random_state=0)
gcv = GridSearchCV(model,
param_grid={
"support_size": support_size,
"alpha": alpha
},
cv=cv,
n_jobs=5).fit(data.x, data.y)
assert gcv.best_params_["support_size"] == k
assert gcv.best_params_["alpha"] == 0.
def test_poisson(self):
# to do
n = 100
p = 20
k = 3
family = "poisson"
rho = 0.5
sigma = 1
M = 1
np.random.seed(3)
data = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
data2 = gen_data_splicing(family=family, n=n, p=p, k=k, rho=rho, M=M)
support_size = range(0, 20)
model = abessPoisson(path_type="seq",
support_size=support_size,
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=10,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=True,
ic_coef=1.,
thread=5,
sparse_matrix=True)
group = np.linspace(1, p, p)
model.fit(data.x, data.y, group=group)
model2 = abessPoisson(path_type="seq",
support_size=support_size,
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=80,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=False,
ic_coef=1.,
thread=5)
group = np.linspace(1, p, p)
model2.fit(data.x, data.y, group=group)
model2.predict(data.x)
nonzero_true = np.nonzero(data.coef_)[0]
nonzero_fit = np.nonzero(model2.coef_)[0]
print(nonzero_true)
print(nonzero_fit)
assert (nonzero_true == nonzero_fit).all()
if sys.version_info[1] >= 6:
new_x = data.x[:, nonzero_fit]
reg = PoissonRegressor(alpha=0, tol=1e-6, max_iter=200)
reg.fit(new_x, data.y)
print(model2.coef_[nonzero_fit])
print(reg.coef_)
assert model2.coef_[nonzero_fit] == approx(reg.coef_,
rel=1e-2,
abs=1e-2)
def test_gaussian(self):
n = 100
p = 20
k = 3
family = "gaussian"
rho = 0.5
sigma = 1
M = 1
# np.random.seed(2)
data = gen_data_splicing(family=family, n=n, p=p, k=k, rho=rho, M=M)
data2 = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
data3 = gen_data_splicing(family=family,
n=n,
p=p,
k=k,
rho=rho,
M=M,
sparse_ratio=0.1)
s_max = 20
model = abessLm(path_type="seq",
support_size=range(0, s_max),
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=10,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=True,
ic_coef=1.,
thread=5,
covariance_update=True)
model.fit(data.x, data.y)
model.predict(data.x)
model2 = abessLm(path_type="seq",
support_size=range(0, s_max),
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=10,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=True,
ic_coef=1.,
thread=1,
covariance_update=True)
model2.fit(data.x, data.y)
model3 = abessLm(path_type="seq",
support_size=range(0, s_max),
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=10,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=True,
ic_coef=1.,
thread=0,
covariance_update=False,
sparse_matrix=True)
model3.fit(data.x, data.y)
model4 = abessLm(path_type="seq",
support_size=range(0, s_max),
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=False,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=10,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=True,
ic_coef=1.,
thread=0,
covariance_update=True)
model4.fit(data.x, data.y)
nonzero_true = np.nonzero(data.coef_)[0]
nonzero_fit = np.nonzero(model.coef_)[0]
print(nonzero_true)
print(nonzero_fit)
new_x = data.x[:, nonzero_fit]
reg = LinearRegression()
reg.fit(new_x, data.y.reshape(-1))
assert model.coef_[nonzero_fit] == approx(reg.coef_,
rel=1e-5,
abs=1e-5)
assert (nonzero_true == nonzero_fit).all()
def test_cox(self):
n = 100
p = 20
k = 3
family = "cox"
rho = 0.5
sigma = 1
# np.random.seed(3)
np.random.seed(3)
data = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
support_size = range(0, 20)
model = abessCox(path_type="seq",
support_size=support_size,
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=30,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=True,
ic_coef=1.,
thread=5)
group = np.linspace(1, p, p)
model.fit(data.x, data.y, group=group)
model.predict(data.x)
model2 = abessCox(path_type="seq",
support_size=support_size,
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=60,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=False,
ic_coef=1.,
thread=5,
sparse_matrix=True)
group = np.linspace(1, p, p)
model2.fit(data.x, data.y, group=group)
nonzero_true = np.nonzero(data.coef_)[0]
nonzero_fit = np.nonzero(model2.coef_)[0]
print(nonzero_true)
print(nonzero_fit)
assert (nonzero_true == nonzero_fit).all()
if sys.version_info[1] >= 6:
new_x = data.x[:, nonzero_fit]
survival = pd.DataFrame()
for i in range(new_x.shape[1]):
survival["Var" + str(i)] = new_x[:, i]
survival["T"] = data.y[:, 0]
survival["E"] = data.y[:, 1]
cph = CoxPHFitter(penalizer=0, l1_ratio=0)
cph.fit(survival, 'T', event_col='E')
print(model2.coef_[nonzero_fit])
print(cph.params_.values)
assert model2.coef_[nonzero_fit] == approx(cph.params_.values,
rel=5e-1,
abs=5e-1)
def test_binomial(self):
n = 100
p = 20
k = 3
family = "binomial"
rho = 0.5
sigma = 1
np.random.seed(1)
data = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
support_size = range(0, 20)
print("logistic abess")
model = abessLogistic(path_type="seq",
support_size=support_size,
ic_type='ebic',
is_screening=False,
screening_size=30,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=10,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=False,
ic_coef=1.,
thread=5)
group = np.linspace(1, p, p)
model.fit(data.x, data.y, group=group)
model2 = abessLogistic(path_type="seq",
support_size=support_size,
ic_type='ebic',
is_screening=True,
screening_size=20,
K_max=10,
epsilon=10,
powell_path=2,
s_min=1,
s_max=p,
lambda_min=0.01,
lambda_max=100,
is_cv=True,
K=5,
exchange_num=2,
tau=0.1 * np.log(n * p) / n,
primary_model_fit_max_iter=80,
primary_model_fit_epsilon=1e-6,
early_stop=False,
approximate_Newton=False,
ic_coef=1.,
thread=5,
sparse_matrix=True)
group = np.linspace(1, p, p)
model2.fit(data.x, data.y, group=group)
model2.predict(data.x)
nonzero_true = np.nonzero(data.coef_)[0]
nonzero_fit = np.nonzero(model2.coef_)[0]
print(nonzero_true)
print(nonzero_fit)
assert (nonzero_true == nonzero_fit).all()
if sys.version_info[1] >= 6:
new_x = data.x[:, nonzero_fit]
reg = LogisticRegression(penalty="none")
reg.fit(new_x, data.y)
print(model2.coef_[nonzero_fit])
print(reg.coef_)
assert model2.coef_[nonzero_fit] == approx(reg.coef_[0],
rel=1e-2,
abs=1e-2)